SETUP

Load packages and paths

library(grid)
library(ggplot2)
library(plyr)
library(RColorBrewer)

Color Scheme

CMI Recommendations

cmi_main_blue = "#0071b2"
cmi_grey = "#929d9e"
cmi_light_blue = "#00c4d9"
cmi_pea_green = "#b5bf00"

cmi_rich_green = "#73933d"
cmi_rich_purple = "#8e7fac"
cmi_rich_red = "#d75920"
cmi_rich_blue = "#4c87a1"
cmi_rich_aqua = "#66c7c3"
cmi_rich_orange = "#eebf42"

cmi_vibrant_yellow = "#ffd457"
cmi_vibrant_orange = "#f58025"
cmi_vibrant_green = "#78a22f"
cmi_vibrant_garnet = "#e6006f"
cmi_vibrant_purple = "#9A4d9e"
cmi_vibrant_blue = "#19398a"

cmi_site_colors = c(cmi_vibrant_blue, cmi_rich_blue, cmi_vibrant_purple, cmi_vibrant_garnet, 
    cmi_rich_red, cmi_vibrant_orange, cmi_vibrant_yellow, cmi_vibrant_green)
cmi_site_colors_ramp = colorRampPalette(cmi_site_colors)

Load data

Read in the data and then some


# setwd('~/zarrar') setwd('~/Dropbox/Research/cmi/')
# setwd('qc/corr.scripts')

# script.dir <- dirname(sys.frame(1)$ofile) setwd(script_dir)

df <- read.csv("../corr.qc/qc_filt_epi_derivatives_smoothed_cpac.csv")[, -1]
nsites <- length(unique(df$site))

Percentiles

In our plots, we want to have percentile lines for each QC measure to indicate the distribution of each site relative to the whole sample

qc.measures <- colnames(df)[!(colnames(df) %in% c("uniqueid", "subject", "subid", 
    "site", "site.name", "session", "scan", "global"))]
qvals <- c(0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99)
qcat <- c(1, 5, 25, 50, 25, 5, 1)
qline <- c(3, 2, 5, 1, 5, 2, 3)
qsize <- c(0.4, 0.25, 0.3, 0.25, 0.3, 0.25, 0.4)
qcols <- c("grey10", "grey10", "grey10", "grey50", "grey10", "grey10", "grey10")
# qcols <- brewer.pal(8, 'Dark2')[c(1,2,3,4,3,2,1)]

Now let's get the percentiles

percentiles <- apply(subset(df, select = qc.measures), 2, quantile, qvals, na.rm = TRUE)
percentiles <- as.data.frame(cbind(percentiles, qcat, qline, qsize))
percentiles$qline <- as.factor(qline)
percentiles$qcat <- as.factor(qcat)
print(percentiles)

##     falff_50 falff_75 falff_90 falff_fwhm falff_mean reho_50 reho_75
## 1%    0.3838   0.4038   0.4280      6.459     0.3813 0.05749 0.07110
## 5%    0.4945   0.5254   0.5559      6.782     0.4880 0.06762 0.08962
## 25%   0.6051   0.6294   0.6564      7.093     0.5955 0.09342 0.12988
## 50%   0.6300   0.6650   0.6985      7.284     0.6259 0.11382 0.15979
## 75%   0.6716   0.7058   0.7381      7.591     0.6625 0.15200 0.20972
## 95%   0.7372   0.7554   0.7763      8.171     0.7182 0.21227 0.28739
## 99%   0.7479   0.7710   0.7964      8.700     0.7306 0.24326 0.32237
##     reho_90 reho_fwhm reho_mean vmhc_50 vmhc_75 vmhc_90 vmhc_fwhm
## 1%  0.08866     9.031   0.06265  0.1776  0.3422  0.5338     5.393
## 5%  0.11621    10.975   0.07593  0.2355  0.4187  0.6079     5.803
## 25% 0.16977    13.115   0.10428  0.3217  0.5170  0.6864     6.255
## 50% 0.20697    14.113   0.12494  0.3919  0.5826  0.7375     6.465
## 75% 0.26642    14.890   0.16334  0.4566  0.6427  0.7841     6.649
## 95% 0.35875    15.938   0.22444  0.5579  0.7276  0.8455     6.900
## 99% 0.39790    16.975   0.25374  0.6078  0.7767  0.8755     7.081
##     vmhc_mean qcat qline qsize
## 1%     0.2136    1     3  0.40
## 5%     0.2625    5     2  0.25
## 25%    0.3293   25     5  0.30
## 50%    0.3801   50     1  0.25
## 75%    0.4260   25     5  0.30
## 95%    0.4957    5     2  0.25
## 99%    0.5303    1     3  0.40

Measure Descriptions

Associate a detailed description with each measure

dnames <- c("fALFF", "REHO", "VMHC")
mnames <- c("Median", "75th Percentile", "90th Percentile", "FWHM (mm)", "Mean")
descs <- expand.grid(m = mnames, d = dnames)
descs <- paste(descs[, 2], descs[, 1], sep = " - ")
mdf <- data.frame(measure = qc.measures, description = descs)
print(mdf)

##       measure             description
## 1    falff_50          fALFF - Median
## 2    falff_75 fALFF - 75th Percentile
## 3    falff_90 fALFF - 90th Percentile
## 4  falff_fwhm       fALFF - FWHM (mm)
## 5  falff_mean            fALFF - Mean
## 6     reho_50           REHO - Median
## 7     reho_75  REHO - 75th Percentile
## 8     reho_90  REHO - 90th Percentile
## 9   reho_fwhm        REHO - FWHM (mm)
## 10  reho_mean             REHO - Mean
## 11    vmhc_50           VMHC - Median
## 12    vmhc_75  VMHC - 75th Percentile
## 13    vmhc_90  VMHC - 90th Percentile
## 14  vmhc_fwhm        VMHC - FWHM (mm)
## 15  vmhc_mean             VMHC - Mean

cat("CHECK ABOVE...do the columns for each row match\n")

## CHECK ABOVE...do the columns for each row match

PLOTS

More Setup

This function will add percentile lines in the background plot: ggplot object pdf: percentile data frame

compile_percentiles <- function(pdf, measure, cols = NULL) {
    ret <- lapply(1:nrow(pdf), function(i) {
        p <- pdf[i, ]
        if (!is.null(cols)) {
            plot <- geom_hline(aes_string(yintercept = measure), data = p, size = as.numeric(p$qsize), 
                linetype = as.numeric(p$qline), color = cols[i])
            # as.character(p$qcolor[1])
        } else {
            plot <- geom_hline(aes_string(yintercept = measure), data = p, size = as.numeric(p$qsize[1]), 
                linetype = as.numeric(p$qline[1]), color = "grey50")
        }
        return(plot)
    })
    return(ret)
}

Outliers

Sometimes extreme data-points can skew the plot and make it difficult to see the spread of the data.

I will avoid plotting those outlier points by setting the axis to only the points that I want. ggplot will complain but let her (poor baby).

# functions
range.outlier.iqr <- function(x, times = 3) {
    upper.limit <- quantile(x, 0.75) + times * IQR(x)
    lower.limit <- quantile(x, 0.25) - times * IQR(x)
    return(c(lower.limit, upper.limit))
}
outlier.iqr <- function(x, times = 3) {
    tmp <- range.outlier.iqr(x, times)
    lower.limit <- tmp[1]
    upper.limit <- tmp[2]
    return((x > upper.limit) | (x < lower.limit))
}
# outlier values (if any)
lst.outlier.iqr <- llply(qc.measures, function(measure) {
    ret <- subset(df, select = c("subject", "site", "site.name", "session", 
        "scan", measure))
    inds <- outlier.iqr(df[[measure]])
    return(ret[inds, ])
})
names(lst.outlier.iqr) <- qc.measures
# new ranges of our plots (sans outliers)
df.range.iqr <- as.data.frame(sapply(qc.measures, function(m) {
    inds <- !outlier.iqr(df[[m]])
    range(df[[m]][inds]) * c(0.99, 1.01)
}))

Visualization of Text

A function with all the theme jazz

set_themes <- function(family = "Times", text.size.x = 14, text.size.y = 16, 
    title.size = 18) {
    family <- "sans"
    pg <- list(theme_bw(), theme(axis.title.x = element_text(family = family, 
        face = "plain", size = title.size)), theme(axis.title.y = element_text(family = family, 
        face = "plain", size = title.size, angle = 90, vjust = 0.25)), theme(axis.text.x = element_text(family = family, 
        face = "plain", size = text.size.x, vjust = 0.5, angle = 45)), theme(axis.text.y = element_text(family = family, 
        face = "plain", size = text.size.y, angle = 90)), theme(axis.ticks.length = unit(0.15, 
        "lines")), theme(axis.ticks.margin = unit(0.15, "lines")), theme(plot.margin = unit(c(0.25, 
        1, 0.25, 1), "lines")), theme(legend.position = "none"))
    return(pg)
}

QC Derivative Measures - Smoothed with CPAC

I will be plotting a bunch of different data-sets here. First I will have all the data, then I will have it remote 3 x the IQR. So two sets of the same plots.